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Targeted Proteomics Data

AMP PD features two separate Targeted Proteomics datasets and one Targeted Proteomics release product. AMP PD Release 4.0 contains a bridged data release product generated from Release 3.0 datasets, containing samples from the NINDS Parkinson’s Disease Biomarkers Program (PDBP) and Michael J Fox Foundation’s Parkinson’s Progression Markers Initiative (PPMI). 

Release 3.0 contains two datasets generated using Olink 1536 from two separate data providers, labeled ‘D01’ and ‘D02’. Within the dataset ‘D01’, there were 746 CSF and Plasma samples from 213 participants. Dataset ‘D02’ includes 666 CSF samples & 898 plasma samples from 225 participants matched across both tissue types. This results in a total of 3050 samples from 413 participants. 

These Targeted Proteomics datasets and release product contain eight unfiltered NPX files from four separate Panels for both Plasma and CSF samples. The four targeted proteomics panels are Cardiometabolic, Inflammation, Neurology, and Oncology. 

Release 4.0 contains a bridged data release product that was generated using Olink provided bridging scripts in R. This is available under the Release 4.0 tag within the proteomics folder. In Release 3.0, all datasets are listed under the main datasets tab within the proteomics folder. All data products are split into four separate datasets. The data is split by tissue source (CSF, Plasma), data set number (D01, D02) and release (D03). From there, the main folders for each data set contain data from all four panels in both long and matrix format. An additional format is provided in the olink-explore-format folder. This format is provided for those who wish to use Olink specific tools. Each dataset also contains a sample metadata sheet for user reference.

Data has been reformatted from the original format to contain AMP PD specific participant and sample IDs. Additional QC columns have been added to the data in order to provide additional information for flagged or passed samples. The released data has accompanying Terra notebooks, ranging from “Getting Started” notebooks which help users get familiar with the data and eventually assign case and control to samples, to QC notebooks to show users QC criteria used in flagging samples and analysis notebooks to perform simple data visualizations.

As AMP PD accumulates datasets of the same source tissue and compatible methods, updates to the normalized aggregate release product will be made. Datasets in the datasets directory should not be combined in researchers' analyses without first assessing compatibility and normalizing their values, therefore, users wishing to use NPX values from both ‘D01’ and ‘D02’ datasets should use the Release 4.0 Proteomics Release product.

What's on this page:

Sample Selection Criteria
Method
Assay Control
References

    Baseline 3M 6M 9M 12M 18M 24M 30M 36M 42M 48M 54M 60M 72M 84M 96M Total
PDBP Plasma 111 0 2 0 91 28 114 0 110 0 48 0 17 0 0 0 521
CSF 111 0 2 0 91 28 114 0 110 0 48 0 17 0 0 0 521
PPMI Plasma 263 26 125 9 128 24 166 26 94 18 148 15 55 7 5 1 1120
CSF 244 5 111 0 109 0 152 1 84 0 133 1 43 0 5 1 888

 

Sample Selection Criteria

The criteria used to select these samples were as follows:

  • Samples for three timepoint or more available
  • Participant samples selected were from participants who had previously generated corresponding Whole Genome Sequencing or Transcriptomic data on the AMP PD Knowledge Platform
  • All CSF samples selected had hemoglobin < 100 ng/mL to assure limited blood contamination
  • All Plasma and CSF samples were collected under similar protocols

AMP PD Quality control of the preview release data was performed by Victoria Dardov from Technome as part of a contract with the Foundation for the National Institutes of Health (FNIH).

Information here was prepared by the Olink Proteomics in consultation with the AMP PD Proteomics Working Group.

Abbreviations

CoA    Certificate of Analysis
CSF    Cerebrospinal Fluid
fixSD    A predefined standard deviation    
LOD    Limit of detection
MAD    Median absolute deviation
NPX    Normalized protein expression
PC    Plate control
PEA    Proximity Extension Assay
NGS    Next Generation Sequencing

Method

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Proximity Extension Assay for Targeted Proteomics

Proximity Extension Assay for Targeted Proteomics

Normalized Protein Expression (NPX) quantifies the relative amount of a specific protein. This is determined by performing an immunoassay for a targeted protein. Antibodies that bind to a protein of interest contain unique sequences that are extended, amplified and subsequently detected and quantified by NGS. The amount of this sequence is normalized to standard plate controls to give relative quantities of targeted proteins. 

Assay Controls Summary

Extensive quality control is performed for each assay in order to control and assess technical performance of the assay at each step. This ensures generation of reliable data. 

AMP PD Controls

AMP-PD quality control further examines the data and includes sample, run and control sample QC. 

Generating NPX values 

The Explore system´s raw data output are NGS counts, where each combination of an assay and sample is given an integer value based on the number of DNA copies detected. These raw data counts are converted into NPX values for use in downstream statistical analysis.  

Assay Controls

We have an extensive quality control procedure that we follow when running an assay. This allows for full visibility and control over the technical performance of the assay at each step and ensures that reliable data are generated with customers' samples. 

Internal controls
Three internal controls are added to each sample to monitor the quality of assay performance, as well as the quality of individual samples: 

  • An Incubation Control, which is a PEA assay for spiked-in non-human proteins.
  • An Extension Control, which is an antibody molecule with both DNA-oligos attached and therefore always in proximity.
  • An Amplification Control, which is a synthetic double-stranded amplicon. 
  • The extension control is used to calculate the NPX, and the other two are used for quality control of the assay.

External control
We also include external control samples on each plate to normalize data and also to monitor the assay’s performance:

  • A Sample Control of pooled plasma to estimate intra- and inter-assay precision. 
  • A Negative Control to measure background levels, which is used to estimate LOD for all assays.
  • A Plate Control of pooled plasma to calculate the NPX and normalize signal levels between different plates for each assay.

 

Converting Counts to NPX

The Explore system´s raw data output are NGS counts, where each combination of an assay and sample is given an integer value based on the number of DNA copies detected. These raw data counts are converted into NPX values for use in downstream statistical analysis. 

NPX generation and Normalization
The NPX values are calculated in two main steps and then intensity normalized by performing between-plate-normalization. First, the assay counts of a sample are divided by those of the extension control for that sample and block, which then undergoes log2 transformation to normalize the data:

Steps in the NPX generation described in equation form, where “i” refers to a specific assay, j refers to a sample, and ExtNPX defines an extension normalized NPX value.
 

ExtNPX_i, j = log2(counts(sample_jAssay_i)/counts (ExtCtrl_j))

  • Relate counts to a known standard (Extension control)
  • For all assays and all samples, including negative controls, Plate Controls, and Sample controls.
  • Log2 transformation gives more normally distributed data

The result is a scale that has increasing sample values against increasing protein concentration for each assay. The median of the Plate Control is then subtracted from the normalized data:

NPX_i, j = ExtNPX_i, j-median (ExtNPX(Plate Controls_i))

  • Perform plate standardization
  • For all assays and per plate of samples

Intensity normalization of data is the default for randomized studies on multiple sample plates. In this setting, the median for a random selection of samples is more stable across plates than the three PC’s on each plate. Intensity normalization sets the median level of all assays to the same value for all plates:

NPX_Intnorm_i,j = (NPX_i,j – plate median(NPX_i)) + global median(NPX_i) 

  • Between plate normalization (Optional but the default for multi-plate projects)
  • For each assay, for all plates in the project.

Steps involved in NPX generation and data normalization
Figure 1: Summary of the three steps involved in NPX generation and data normalization. Step 1 is illustrated by the red graph, step 2 by the yellow graph, and step 3 (data normalization) by the turquoise graph. GHRL refers to appetite-regulating hormone (Uniprot ID: Q9UBU3). 

AMP PD Quality Controls

Sample QC
Each sample in a block is given the status pass or warning. For a complete Explore 1536 run, this means that each sample is evaluated 16 times. There are three sample QC criteria per block. Exceeding specifications in any one of them will result in a QC warning status for that sample. The three criteria are listed below:

  1. Incubation control deviation from the median may not exceed 0.3 NPX.
  2. Amplification control deviation from the median may not exceed 0.3 NPX.
  3. Average counts for a sample may not fall below 500 counts. 

Data from samples with a warning should be treated with caution.
 

Run QC 
In addition to specific samples that exceed specifications, the entire block can be considered failed if it meets criteria 1 and 2 below. For practical reasons, this will most likely result in the re-run of a panel. If criteria 3 below is met the panel is failed:

  1. The number of samples with QC warning exceeds 16/88 or 1/6. 
  2. The MAD for incubation or amplification control across all samples exceeds 0.3.
  3. The Plate Control and Negative Control exceeds the criteria specified in section Control strip QC.

Control strip QC
The Plate Controls and negative controls have specific QC criteria. The median of the triplicates may not deviate more than 3 standard deviations from predefined tolerance levels for more than 10% of the assays on average for a panel. For the negative control, only positive deviations are considered. 

LOD and CV Calculation

LOD is defined as being three standard deviations above the median NPX of negative controls. The median is set using all samples annotated as negative controls per plate. A predefined standard deviation is used (fixSD). Detectability is calculated per assay and plate and is defined by the percentage of samples above the LOD threshold. The overall detectability of the project is generated and reported in the CoA:

The CV is calculated per assay (i) using the assumption of a log-normal distribution. The average CV is then calculated across panels and included in the CoA output. 

CVi

Bridging Datasets Methodology

Inclusion of Bridging Samples:

The final goal was to perform a joint analysis of datasets D01 and D02, and it was recommended to use common samples in the two batches that could help with batch correction to remove technical variation between the runs. Olink recommends the inclusion of 16-24 of these “bridging samples” across runs and the protein levels measured for these samples can be used as a common reference between batches, used to normalize and alleviate any potential bias. In this experiment, 39 samples from the D01 batch were included as bridging samples with the D02 dataset. These samples were selected using the dplyr R package to find the intersecting samples between D01 and D02 datasets. This list of overlapping samples was used as part of the olink_normalization_bridge_function from the Olink Analyze R package. 

Bridging Data and QC

The bridging procedure involved the calculation of the median of the paired NPX differences per assay between the bridging samples. This is the assay specific adjustment factor which is used to adjust NPX values between all remaining samples in the two datasets. In this process, the D01 dataset was considered the reference dataset and its NPX values remained unaltered. The D02 dataset was adjusted to the reference dataset based on the adjustment factors. 

In some cases, the bridging led to the duplication of the same sample x uniprot with two separate NPX values. In addition, there are cases in which there is a duplicate uniprot ID due to the same uniprot ID being in more than one Olink panel or there being two different Olink assays that map to that uniprot ID. In all of these cases, the mean NPX value was calculated and used in the final data product, resulting in one NPX value for every uniprot ID for every sample ID. 

The bridging and mean calculations were all conducted using R and were performed separately for cerebrospinal fluid data sets and the plasma data sets. Results of the bridging can be seen below through PCA. Figure 1a shows dataset D01 and D02 prior to bridging. Separation along PC1 shows datasets D01 and D02 as distinct, with 37.91% of the variation due to batch effects. Figure 1b shows that after performing bridging, D01 and D02 no longer show separation along PC1, indicating that bridging was successful and batch effects were removed. 
Figure 1a shows dataset D01 and D02 prior to bridging. Separation along PC1 shows datasets D01 and D02 as distinct, with 37.91% of the variation due to batch effects.Figure 1b shows that after performing bridging, D01 and D02 no longer show separation along PC1, indicating that bridging was successful and batch effects were removed.